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Record Nr. |
UNINA9910346783603321 |
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Autore |
Huber Marco |
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Titolo |
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications |
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Pubbl/distr/stampa |
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KIT Scientific Publishing, 2015 |
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ISBN |
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Descrizione fisica |
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1 electronic resource (V, 270 p. p.) |
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Collana |
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Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems. |
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